Training DHMMs of mine and clutter to minimize landmine detection errors

Minimum classification error (MCE) training is proposed to improve performance of a discrete hidden Markov model (DHMM)-based landmine detection system. The system (baseline) was proposed previously for detection of both metal and nonmetal mines from ground-penetrating radar signatures collected by moving vehicles. An initial DHMM model is trained by conventional methods of vector quantization and the Baum-Welch algorithm. A sequential generalized probabilistic descent (GPD) algorithm that minimizes an empirical loss function is then used to estimate the landmine/background DHMM parameters, and an evolutionary algorithm (EA) based on fitness score of classification accuracy is used to generate and select codebooks. The landmine data of one geographical site was used for model training, and those of two different sites were used for evaluation of system performance. Three scenarios were studied: 1) apply MCE/GPD alone to DHMM estimation, 2) apply EA alone to codebook generation, and 3) first apply EA to codebook generation and then apply MCE/GPD to DHMM estimation. Overall, the combined EA and MCE/GPD training led to the best performance. At the same level of detection rate as the baseline DHMM system, the false-alarm rate was reduced by a factor of two, indicating significant performance improvement.